基于多数据源的机构知识可视化研究与应用

目的/意义]单一数据源下机构知识可视化存在信息的缺失,基于多数据源的可视化可以相辅相成,互为补充。方法/过程]分析了多数据源下机构知识动态可视化的关键技术,并针对知识节点的巨星问题给出一种多粒度数据采样和属性值加权的知识构造方法,对平台的系统架构进行了详细讨论和设计,最后基于Echarts可视化套件对平台中合作关系、研究热点、机构分布等进行了实现。结果 /结论]多数据源知识可视化平台能弥补单一数据源中可能丢失的各种关系,多粒度采样让可视化结果整体美观协调,为机构的学术发展状况、研究方向的确立、研究团队的组建等决策提供了信息支持。

Research and Application of Mechanism Knowledge Visualization Based on Multiple Data Sources

Purpose/Significance] There is a lack of information in organization knowledge visualization under a single data source.Visualization based on multiple data sources can complement each other and complement each other.Method/Process] The key technology of dynamic visualization of mechanism knowledge under multi-data sources was analyzed,and a knowledge construction method of multi-granularity data sampling and attribute value weighting was presented for the superstar problem of knowledge nodes.The system architecture of the platform was discussed and designed in detail.Finally,the platform was implemented based on Echarts visualization suite.Cooperative relations,research hotspots and institutional distribution were realized.Result/Conclusion] Multi-data source knowledge visualization platform could make up for all kinds of relationships that may be lost in a single data source.Multi-data sampling could make the visualization results beautiful and coordinated,and provide information support for the decision-making of academic development,research direction establishment and research team formation.